The present invention relates to the field of image recognition, and more particularly to a method and system for using non-literal imagery exploitation and generic computing techniques for automatic object detection, recognition, and materials identification on multispectral and hyperspectral imagery.
Multispectral and hyperspectral image sets contain large amounts of data that are difficult to analyze by manual means. These image sets include multiple bands of image data that cannot be easily visualized or assessed. Currently, multispectral sensors collect images of a terrain or landscape and provide a handful of spectral bands of imagery, which cover the visible to short wave infrared portion of the electromagnetic spectrum. Similarly, hyperspectral sensors typically provide hundreds of spectral bands of spatial imagery that span a specific portion of the electromagnetic spectrum. As a result, hyperspectral sensors generally provide greater spectral discrimination than those systems using multispectral sensors. Improved spectral discrimination provided by hyperspectral sensors allows non-literal processing of the data to detect and classify materials and objects present in the imagery.
The phrase "non-literal imagery exploitation" covers the process of extracting nonspatial information from imagery to help characterize, detect, locate, classify, discriminate, identify, quantify, predict, track, warn, target or assess emissions, activities or events of interest represented by the image data. Prior techniques for this type of hyperspectral imagery exploitation have used a model based or a least squares approaches to detect and classify materials present in the image data.
These techniques have several shortcomings, however, because they are not robust across different sets of imagery collected from the same sensor at separate times and spatial locations. Further, these methods cannot accurately detect the presence of other materials (i. e., "spectral unmixing") of a pixel spectral signature. In addition, conventional techniques are usually complex and require a great deal of computation. They are also sensitive to sensor and atmospheric variations, and do not easily conform to non-linear sensor, atmospheric and mixing models. Finally, some of the existing multispectral and hyperspectral approaches do not easily scale as the number of endmembers in the detected image is expanded.
It is therefore desirable to have a solution that overcomes the shortcomings of prior imaging and pattern recognition techniques. It is also desirable for a solution that minimizes some of the drawbacks observed with existing methods for hyperspectral imagery exploitation and offer a more robust method of non-literal exploitation.